DenserRadar: A 4D millimeter-wave radar point cloud detector based on dense LiDAR point clouds

TL;DR

This paper introduces DenserRadar, a novel detector that enhances 4D mmWave radar point cloud perception by leveraging dense LiDAR data, significantly improving density and accuracy for autonomous driving in challenging environments.

Contribution

It presents a new method that constructs dense ground truth from LiDAR and employs a specialized network to improve radar point cloud detection accuracy.

Findings

Outperforms existing radar detectors in density and accuracy.

Constructs dense ground truth from stitched LiDAR data.

Achieves superior results on the K-Radar dataset.

Abstract

The 4D millimeter-wave (mmWave) radar, with its robustness in extreme environments, extensive detection range, and capabilities for measuring velocity and elevation, has demonstrated significant potential for enhancing the perception abilities of autonomous driving systems in corner-case scenarios. Nevertheless, the inherent sparsity and noise of 4D mmWave radar point clouds restrict its further development and practical application. In this paper, we introduce a novel 4D mmWave radar point cloud detector, which leverages high-resolution dense LiDAR point clouds. Our approach constructs dense 3D occupancy ground truth from stitched LiDAR point clouds, and employs a specially designed network named DenserRadar. The proposed method surpasses existing probability-based and learning-based radar point cloud detectors in terms of both point cloud density and accuracy on the K-Radar dataset.